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Machine learning is rapidly becoming one of the most important technology formalware traffic detection, since the continuous evolution of malware requires aconstant adaptation and the ability to generalize. However, network trafficdatasets are usually oversized and contain redundant and irrelevantinformation, and this may dramatically increase the computational cost anddecrease the accuracy of most classifiers, with the risk to introduce furthernoise.  We propose two novel dataset optimization strategies which exploit andcombine several state-of-the-art approaches in order to achieve an effectiveoptimization of the network traffic datasets used to train malware detectors.The first approach is a feature selection technique based on mutual informationmeasures and sensibility enhancement. The second is a dimensional reductiontechnique based autoencoders. Both these approaches have been experimentallyapplied on the MTA-KDD'19 dataset, and the optimized results evaluated andcompared using a Multi Layer Perceptron as machine learning model for malwaredetection.

Dataset Optimization Strategies for MalwareTraffic Detection